In this paper we have shown that AdaBoost, as described in Algorithm REF , achieves consistent recovery in the presence of small, adversarial errors, despite being overparameterized and interpolating the observations.
In addition, the derived convergence rates in Corollary REF  are comparable to convergence rates of state-of-the-art regularized estimators [1]}. This is a first step for the understanding of overparameterized and interpolating AdaBoost and other interpolating algorithms and shows why such algorithms can generalize well in high-dimensional and noisy situations, despite interpolating the data.
